Mr. Wonhyung Kim Profile

Wonhyung Kim

at Konyang Univ

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Publications (2)

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Proceedings Article | 1 March 2019 Paper

Improvement of material decomposition accuracy using denoising and deblurring techniques in spectral mammography

Jisoo Eom, Burnyoung Kim, Wonhyung Kim, Youngjin Lee, Seungwan Lee

Proceedings Volume 10948, 109485N (2019) https://doi.org/10.1117/12.2512680

KEYWORDS: Denoising, Modulation transfer functions, Spatial resolution, Mammography, Image quality, Digital mammography, Monte Carlo methods, Image processing

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With an increase of breast cancer patients, dual-energy mammographic techniques have been advanced for improving diagnostic accuracy. In general, conventional dual-energy techniques increase radiation dose because the techniques are based on double exposures. Dual-energy techniques with photon-counting detectors (PCDs) can be implemented by using a single exposure. However, the images obtained from the dual-energy techniques with the PCDs suffer from statistical noise because the dual-energy measurements were performed with a single exposure, causing a lack of the number of effective photons. Thus, the material decomposition accuracy is decreased, and the image quality is distorted. In this study, denoising and deblurring techniques were iteratively applied to a dual-energy mammographic technique based on a PCD, and we evaluated RMSE, noise, and CNR for the quantitative analysis of material decomposition. The results showed that the RMSE value was about 0.23 times lower for the decomposed images with the denoising and deblurring techniques than that without the denoising and deblurring techniques. The noise and CNR of the decomposed images were averagely decreased and increased by factors of 0.23 and 4.17, respectively, through the denoising and deblurring techniques. But, the iterative application of the debelurring technique slightly increased the RMSE and noise. Therefore, it is considered that the material decomposition accuracy and image quality can be improved by applying the denoising and deblurring techniques with the appropriate iterations.

Proceedings Article | 1 March 2019 Paper

Preliminary study on optimization of the stationary inverse-geometry digital tomosynthesis: x-ray source array

Burnyoung Kim, Jisoo Eom, Wonhyung Kim, Seungwan Lee

Proceedings Volume 10948, 109485D (2019) https://doi.org/10.1117/12.2512356

KEYWORDS: X-ray sources, Image quality, X-rays, Data corrections, CT reconstruction, Imaging systems, Systems modeling

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Digital tomosynthesis (DT) improves the diagnostic accuracy compared with 2D radiography due to the good depth resolution. In addition, the DT can reduce radiation dose by more than 80% compared to computed tomography (CT) owing to the scans with limited angles. However, the conventional DT systems have the disadvantages such as geometric complexity and low efficiency. Moreover, the movements of source and detector cause motion artifacts in reconstructed images. Therefore, with the stationary X-ray source and detector, it is possible to reduce the artifacts by simplifying the geometry while preserving the advantages of DT imaging. Also, the geometric inversion with a small detector allows the more efficient diagnosis because fields-of-view (FOVs) can be smaller than the conventional DT systems. The purpose of this study was to develop the stationary inverse-geometry digital tomosynthesis (s-IGDT) imaging technique and compare image quality for linear and curved X-ray source arrays. The signal-to-noise ratio (SNR) of s-IGDT images obtained by using the linear X-ray source array was averagely 1.84 times higher than that using the curved X-ray source array due to low noise components, but the root-mean-square error (RMSE) was averagely 3.25 times higher. The modulation-transfer function (MTF) and radiation dose of the s-IGDT systems with the linear and curved X-ray source arrays were measured at similar levels. As a result, the s-IGDT system with the linear X-ray source array is superior in terms of SNR and noise property, and the curved X-ray array system is superior in terms of quantitative accuracy.

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